Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations974
Missing cells2504
Missing cells (%)12.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory152.3 KiB
Average record size in memory160.1 B

Variable types

Text6
DateTime2
Categorical9
Numeric3

Alerts

STATE has constant value "Massachusetts"Constant
CITY is highly overall correlated with COUNTY and 3 other fieldsHigh correlation
COUNTY is highly overall correlated with CITY and 1 other fieldsHigh correlation
GENDER is highly overall correlated with PREFIXHigh correlation
LAT is highly overall correlated with CITYHigh correlation
LON is highly overall correlated with CITYHigh correlation
MARITAL is highly overall correlated with PREFIXHigh correlation
PREFIX is highly overall correlated with GENDER and 1 other fieldsHigh correlation
ZIP is highly overall correlated with CITY and 1 other fieldsHigh correlation
DEATHDATE has 820 (84.2%) missing valuesMissing
SUFFIX has 953 (97.8%) missing valuesMissing
MAIDEN has 588 (60.4%) missing valuesMissing
ZIP has 142 (14.6%) missing valuesMissing
Id has unique valuesUnique
ADDRESS has unique valuesUnique
LAT has unique valuesUnique
LON has unique valuesUnique

Reproduction

Analysis started2024-08-02 11:56:48.757330
Analysis finished2024-08-02 11:56:55.271436
Duration6.51 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Id
Text

UNIQUE 

Distinct974
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
2024-08-02T12:56:55.695969image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters35064
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique974 ?
Unique (%)100.0%

Sample

1st row5605b66b-e92d-c16c-1b83-b8bf7040d51f
2nd row6e5ae27c-8038-7988-e2c0-25a103f01bfa
3rd row8123d076-0886-9007-e956-d5864aa121a7
4th row770518e4-6133-648e-60c9-071eb2f0e2ce
5th rowf96addf5-81b9-0aab-7855-d208d3d352c5
ValueCountFrequency (%)
5605b66b-e92d-c16c-1b83-b8bf7040d51f 1
 
0.1%
e6bca1f5-d166-438c-b627-15320eaade3f 1
 
0.1%
e3a022ff-f2e0-87b2-a704-68ee3a715c17 1
 
0.1%
f2203fd5-1a2c-60cd-cae0-1416658880b6 1
 
0.1%
8123d076-0886-9007-e956-d5864aa121a7 1
 
0.1%
770518e4-6133-648e-60c9-071eb2f0e2ce 1
 
0.1%
f96addf5-81b9-0aab-7855-d208d3d352c5 1
 
0.1%
8e9650d1-788a-78f9-4a28-d08f7f95354a 1
 
0.1%
183df435-4190-060e-8f8e-bf63c572b266 1
 
0.1%
720560d4-51da-c38c-ee90-c15935278df1 1
 
0.1%
Other values (964) 964
99.0%
2024-08-02T12:56:56.452480image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 3896
 
11.1%
8 2059
 
5.9%
0 2034
 
5.8%
1 2007
 
5.7%
f 1974
 
5.6%
9 1971
 
5.6%
3 1964
 
5.6%
d 1939
 
5.5%
5 1928
 
5.5%
b 1928
 
5.5%
Other values (7) 13364
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35064
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 3896
 
11.1%
8 2059
 
5.9%
0 2034
 
5.8%
1 2007
 
5.7%
f 1974
 
5.6%
9 1971
 
5.6%
3 1964
 
5.6%
d 1939
 
5.5%
5 1928
 
5.5%
b 1928
 
5.5%
Other values (7) 13364
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35064
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 3896
 
11.1%
8 2059
 
5.9%
0 2034
 
5.8%
1 2007
 
5.7%
f 1974
 
5.6%
9 1971
 
5.6%
3 1964
 
5.6%
d 1939
 
5.5%
5 1928
 
5.5%
b 1928
 
5.5%
Other values (7) 13364
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35064
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 3896
 
11.1%
8 2059
 
5.9%
0 2034
 
5.8%
1 2007
 
5.7%
f 1974
 
5.6%
9 1971
 
5.6%
3 1964
 
5.6%
d 1939
 
5.5%
5 1928
 
5.5%
b 1928
 
5.5%
Other values (7) 13364
38.1%
Distinct880
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
Minimum1922-03-24 00:00:00
Maximum1991-11-27 00:00:00
2024-08-02T12:56:56.829395image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-02T12:56:57.228534image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

DEATHDATE
Date

MISSING 

Distinct148
Distinct (%)96.1%
Missing820
Missing (%)84.2%
Memory size7.7 KiB
Minimum2011-02-03 00:00:00
Maximum2022-01-27 00:00:00
2024-08-02T12:56:57.674885image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-02T12:56:58.078144image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PREFIX
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
Mr.
494 
Mrs.
386 
Ms.
94 

Length

Max length4
Median length3
Mean length3.3963039
Min length3

Characters and Unicode

Total characters3308
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMrs.
2nd rowMr.
3rd rowMr.
4th rowMr.
5th rowMr.

Common Values

ValueCountFrequency (%)
Mr. 494
50.7%
Mrs. 386
39.6%
Ms. 94
 
9.7%

Length

2024-08-02T12:56:58.466164image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T12:56:58.715605image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
mr 494
50.7%
mrs 386
39.6%
ms 94
 
9.7%

Most occurring characters

ValueCountFrequency (%)
M 974
29.4%
. 974
29.4%
r 880
26.6%
s 480
14.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3308
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 974
29.4%
. 974
29.4%
r 880
26.6%
s 480
14.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3308
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 974
29.4%
. 974
29.4%
r 880
26.6%
s 480
14.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3308
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 974
29.4%
. 974
29.4%
r 880
26.6%
s 480
14.5%

FIRST
Text

Distinct842
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
2024-08-02T12:56:59.276992image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length16
Median length14
Mean length8.8737166
Min length5

Characters and Unicode

Total characters8643
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique721 ?
Unique (%)74.0%

Sample

1st rowNikita578
2nd rowZane918
3rd rowQuinn173
4th rowAbel832
5th rowEdwin773
ValueCountFrequency (%)
josé 7
 
0.7%
domenic627 3
 
0.3%
armando772 3
 
0.3%
lazaro919 3
 
0.3%
bernardo699 3
 
0.3%
travis723 3
 
0.3%
yolanda648 3
 
0.3%
beatriz277 3
 
0.3%
chris95 3
 
0.3%
emilio366 3
 
0.3%
Other values (836) 951
96.5%
2024-08-02T12:57:00.232529image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 690
 
8.0%
e 606
 
7.0%
n 494
 
5.7%
i 452
 
5.2%
r 450
 
5.2%
o 360
 
4.2%
l 342
 
4.0%
4 328
 
3.8%
1 322
 
3.7%
6 310
 
3.6%
Other values (57) 4289
49.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8643
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 690
 
8.0%
e 606
 
7.0%
n 494
 
5.7%
i 452
 
5.2%
r 450
 
5.2%
o 360
 
4.2%
l 342
 
4.0%
4 328
 
3.8%
1 322
 
3.7%
6 310
 
3.6%
Other values (57) 4289
49.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8643
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 690
 
8.0%
e 606
 
7.0%
n 494
 
5.7%
i 452
 
5.2%
r 450
 
5.2%
o 360
 
4.2%
l 342
 
4.0%
4 328
 
3.8%
1 322
 
3.7%
6 310
 
3.6%
Other values (57) 4289
49.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8643
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 690
 
8.0%
e 606
 
7.0%
n 494
 
5.7%
i 452
 
5.2%
r 450
 
5.2%
o 360
 
4.2%
l 342
 
4.0%
4 328
 
3.8%
1 322
 
3.7%
6 310
 
3.6%
Other values (57) 4289
49.6%

LAST
Text

Distinct498
Distinct (%)51.1%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
2024-08-02T12:57:00.828875image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length16
Median length14
Mean length9.4681725
Min length6

Characters and Unicode

Total characters9222
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique219 ?
Unique (%)22.5%

Sample

1st rowErdman779
2nd rowHodkiewicz467
3rd rowMarquardt819
4th rowSmitham825
5th rowLabadie908
ValueCountFrequency (%)
heaney114 6
 
0.6%
weissnat378 5
 
0.5%
heidenreich818 5
 
0.5%
gleason633 5
 
0.5%
trantow673 5
 
0.5%
rempel203 5
 
0.5%
tillman293 5
 
0.5%
mcclure239 5
 
0.5%
yost751 5
 
0.5%
schowalter414 5
 
0.5%
Other values (489) 924
94.8%
2024-08-02T12:57:01.719913image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 683
 
7.4%
r 539
 
5.8%
a 504
 
5.5%
n 438
 
4.7%
i 390
 
4.2%
o 370
 
4.0%
l 345
 
3.7%
9 322
 
3.5%
7 318
 
3.4%
1 316
 
3.4%
Other values (58) 4997
54.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9222
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 683
 
7.4%
r 539
 
5.8%
a 504
 
5.5%
n 438
 
4.7%
i 390
 
4.2%
o 370
 
4.0%
l 345
 
3.7%
9 322
 
3.5%
7 318
 
3.4%
1 316
 
3.4%
Other values (58) 4997
54.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9222
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 683
 
7.4%
r 539
 
5.8%
a 504
 
5.5%
n 438
 
4.7%
i 390
 
4.2%
o 370
 
4.0%
l 345
 
3.7%
9 322
 
3.5%
7 318
 
3.4%
1 316
 
3.4%
Other values (58) 4997
54.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9222
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 683
 
7.4%
r 539
 
5.8%
a 504
 
5.5%
n 438
 
4.7%
i 390
 
4.2%
o 370
 
4.0%
l 345
 
3.7%
9 322
 
3.5%
7 318
 
3.4%
1 316
 
3.4%
Other values (58) 4997
54.2%

SUFFIX
Categorical

MISSING 

Distinct3
Distinct (%)14.3%
Missing953
Missing (%)97.8%
Memory size7.7 KiB
PhD
10 
JD
MD

Length

Max length3
Median length2
Mean length2.4761905
Min length2

Characters and Unicode

Total characters52
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhD
2nd rowJD
3rd rowPhD
4th rowJD
5th rowJD

Common Values

ValueCountFrequency (%)
PhD 10
 
1.0%
JD 8
 
0.8%
MD 3
 
0.3%
(Missing) 953
97.8%

Length

2024-08-02T12:57:02.050382image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T12:57:02.321267image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
phd 10
47.6%
jd 8
38.1%
md 3
 
14.3%

Most occurring characters

ValueCountFrequency (%)
D 21
40.4%
P 10
19.2%
h 10
19.2%
J 8
 
15.4%
M 3
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 21
40.4%
P 10
19.2%
h 10
19.2%
J 8
 
15.4%
M 3
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 21
40.4%
P 10
19.2%
h 10
19.2%
J 8
 
15.4%
M 3
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 21
40.4%
P 10
19.2%
h 10
19.2%
J 8
 
15.4%
M 3
 
5.8%

MAIDEN
Text

MISSING 

Distinct279
Distinct (%)72.3%
Missing588
Missing (%)60.4%
Memory size7.7 KiB
2024-08-02T12:57:02.824901image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length15
Median length13
Mean length9.4766839
Min length6

Characters and Unicode

Total characters3658
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique198 ?
Unique (%)51.3%

Sample

1st rowLeannon79
2nd rowWiegand701
3rd rowPredovic534
4th rowWalker122
5th rowUpton904
ValueCountFrequency (%)
jerde200 5
 
1.3%
kshlerin58 4
 
1.0%
muller251 3
 
0.8%
senger904 3
 
0.8%
kassulke119 3
 
0.8%
crona259 3
 
0.8%
volkman526 3
 
0.8%
lehner980 3
 
0.8%
yundt842 3
 
0.8%
mante251 3
 
0.8%
Other values (269) 353
91.5%
2024-08-02T12:57:03.665247image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 284
 
7.8%
r 201
 
5.5%
n 193
 
5.3%
a 184
 
5.0%
o 157
 
4.3%
i 144
 
3.9%
l 142
 
3.9%
9 135
 
3.7%
1 129
 
3.5%
s 124
 
3.4%
Other values (58) 1965
53.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3658
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 284
 
7.8%
r 201
 
5.5%
n 193
 
5.3%
a 184
 
5.0%
o 157
 
4.3%
i 144
 
3.9%
l 142
 
3.9%
9 135
 
3.7%
1 129
 
3.5%
s 124
 
3.4%
Other values (58) 1965
53.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3658
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 284
 
7.8%
r 201
 
5.5%
n 193
 
5.3%
a 184
 
5.0%
o 157
 
4.3%
i 144
 
3.9%
l 142
 
3.9%
9 135
 
3.7%
1 129
 
3.5%
s 124
 
3.4%
Other values (58) 1965
53.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3658
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 284
 
7.8%
r 201
 
5.5%
n 193
 
5.3%
a 184
 
5.0%
o 157
 
4.3%
i 144
 
3.9%
l 142
 
3.9%
9 135
 
3.7%
1 129
 
3.5%
s 124
 
3.4%
Other values (58) 1965
53.7%

MARITAL
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing1
Missing (%)0.1%
Memory size7.7 KiB
M
784 
S
189 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters973
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 784
80.5%
S 189
 
19.4%
(Missing) 1
 
0.1%

Length

2024-08-02T12:57:03.978840image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T12:57:04.199810image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
m 784
80.6%
s 189
 
19.4%

Most occurring characters

ValueCountFrequency (%)
M 784
80.6%
S 189
 
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 973
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 784
80.6%
S 189
 
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 973
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 784
80.6%
S 189
 
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 973
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 784
80.6%
S 189
 
19.4%

RACE
Categorical

Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
white
680 
black
163 
asian
91 
other
 
16
hawaiian
 
13

Length

Max length8
Median length5
Mean length5.0513347
Min length5

Characters and Unicode

Total characters4920
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwhite
2nd rowwhite
3rd rowwhite
4th rowwhite
5th rowwhite

Common Values

ValueCountFrequency (%)
white 680
69.8%
black 163
 
16.7%
asian 91
 
9.3%
other 16
 
1.6%
hawaiian 13
 
1.3%
native 11
 
1.1%

Length

2024-08-02T12:57:04.475323image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T12:57:04.794498image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
white 680
69.8%
black 163
 
16.7%
asian 91
 
9.3%
other 16
 
1.6%
hawaiian 13
 
1.3%
native 11
 
1.1%

Most occurring characters

ValueCountFrequency (%)
i 808
16.4%
h 709
14.4%
t 707
14.4%
e 707
14.4%
w 693
14.1%
a 395
8.0%
b 163
 
3.3%
l 163
 
3.3%
c 163
 
3.3%
k 163
 
3.3%
Other values (5) 249
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 808
16.4%
h 709
14.4%
t 707
14.4%
e 707
14.4%
w 693
14.1%
a 395
8.0%
b 163
 
3.3%
l 163
 
3.3%
c 163
 
3.3%
k 163
 
3.3%
Other values (5) 249
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 808
16.4%
h 709
14.4%
t 707
14.4%
e 707
14.4%
w 693
14.1%
a 395
8.0%
b 163
 
3.3%
l 163
 
3.3%
c 163
 
3.3%
k 163
 
3.3%
Other values (5) 249
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 808
16.4%
h 709
14.4%
t 707
14.4%
e 707
14.4%
w 693
14.1%
a 395
8.0%
b 163
 
3.3%
l 163
 
3.3%
c 163
 
3.3%
k 163
 
3.3%
Other values (5) 249
 
5.1%

ETHNICITY
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
nonhispanic
783 
hispanic
191 

Length

Max length11
Median length11
Mean length10.411704
Min length8

Characters and Unicode

Total characters10141
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownonhispanic
2nd rownonhispanic
3rd rownonhispanic
4th rowhispanic
5th rowhispanic

Common Values

ValueCountFrequency (%)
nonhispanic 783
80.4%
hispanic 191
 
19.6%

Length

2024-08-02T12:57:05.210814image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T12:57:05.529638image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
nonhispanic 783
80.4%
hispanic 191
 
19.6%

Most occurring characters

ValueCountFrequency (%)
n 2540
25.0%
i 1948
19.2%
h 974
 
9.6%
s 974
 
9.6%
p 974
 
9.6%
a 974
 
9.6%
c 974
 
9.6%
o 783
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10141
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 2540
25.0%
i 1948
19.2%
h 974
 
9.6%
s 974
 
9.6%
p 974
 
9.6%
a 974
 
9.6%
c 974
 
9.6%
o 783
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10141
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 2540
25.0%
i 1948
19.2%
h 974
 
9.6%
s 974
 
9.6%
p 974
 
9.6%
a 974
 
9.6%
c 974
 
9.6%
o 783
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10141
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 2540
25.0%
i 1948
19.2%
h 974
 
9.6%
s 974
 
9.6%
p 974
 
9.6%
a 974
 
9.6%
c 974
 
9.6%
o 783
 
7.7%

GENDER
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
M
494 
F
480 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters974
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 494
50.7%
F 480
49.3%

Length

2024-08-02T12:57:05.857698image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T12:57:06.156686image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
m 494
50.7%
f 480
49.3%

Most occurring characters

ValueCountFrequency (%)
M 494
50.7%
F 480
49.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 974
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 494
50.7%
F 480
49.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 974
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 494
50.7%
F 480
49.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 974
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 494
50.7%
F 480
49.3%
Distinct297
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
2024-08-02T12:57:07.009994image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length41
Median length37
Mean length27.022587
Min length15

Characters and Unicode

Total characters26320
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique106 ?
Unique (%)10.9%

Sample

1st rowWakefield Massachusetts US
2nd rowBrookline Massachusetts US
3rd rowGardner Massachusetts US
4th rowRandolph Massachusetts US
5th rowStow Massachusetts US
ValueCountFrequency (%)
massachusetts 845
27.1%
us 845
27.1%
boston 79
 
2.5%
worcester 20
 
0.6%
springfield 20
 
0.6%
bogota 18
 
0.6%
dm 17
 
0.5%
north 17
 
0.5%
saint 17
 
0.5%
somerville 17
 
0.5%
Other values (359) 1223
39.2%
2024-08-02T12:57:08.116647image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4092
15.5%
s 3762
14.3%
a 2379
 
9.0%
t 2287
 
8.7%
e 1685
 
6.4%
h 1138
 
4.3%
u 1108
 
4.2%
c 1023
 
3.9%
S 1000
 
3.8%
M 970
 
3.7%
Other values (55) 6876
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26320
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4092
15.5%
s 3762
14.3%
a 2379
 
9.0%
t 2287
 
8.7%
e 1685
 
6.4%
h 1138
 
4.3%
u 1108
 
4.2%
c 1023
 
3.9%
S 1000
 
3.8%
M 970
 
3.7%
Other values (55) 6876
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26320
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4092
15.5%
s 3762
14.3%
a 2379
 
9.0%
t 2287
 
8.7%
e 1685
 
6.4%
h 1138
 
4.3%
u 1108
 
4.2%
c 1023
 
3.9%
S 1000
 
3.8%
M 970
 
3.7%
Other values (55) 6876
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26320
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4092
15.5%
s 3762
14.3%
a 2379
 
9.0%
t 2287
 
8.7%
e 1685
 
6.4%
h 1138
 
4.3%
u 1108
 
4.2%
c 1023
 
3.9%
S 1000
 
3.8%
M 970
 
3.7%
Other values (55) 6876
26.1%

ADDRESS
Text

UNIQUE 

Distinct974
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
2024-08-02T12:57:08.824099image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length34
Median length27
Mean length21.034908
Min length12

Characters and Unicode

Total characters20488
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique974 ?
Unique (%)100.0%

Sample

1st row510 Little Station Unit 69
2nd row747 Conn Throughway
3rd row816 Okuneva Extension Apt 91
4th row127 Cole Way Unit 95
5th row976 Ziemann Gateway
ValueCountFrequency (%)
unit 159
 
4.1%
apt 157
 
4.1%
suite 153
 
4.0%
path 19
 
0.5%
parade 19
 
0.5%
road 19
 
0.5%
boulevard 18
 
0.5%
lane 17
 
0.4%
trafficway 16
 
0.4%
estate 15
 
0.4%
Other values (1243) 3279
84.7%
2024-08-02T12:57:09.941687image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2897
 
14.1%
e 1389
 
6.8%
a 1107
 
5.4%
t 987
 
4.8%
i 953
 
4.7%
r 941
 
4.6%
n 923
 
4.5%
o 723
 
3.5%
l 622
 
3.0%
u 466
 
2.3%
Other values (52) 9480
46.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20488
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2897
 
14.1%
e 1389
 
6.8%
a 1107
 
5.4%
t 987
 
4.8%
i 953
 
4.7%
r 941
 
4.6%
n 923
 
4.5%
o 723
 
3.5%
l 622
 
3.0%
u 466
 
2.3%
Other values (52) 9480
46.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20488
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2897
 
14.1%
e 1389
 
6.8%
a 1107
 
5.4%
t 987
 
4.8%
i 953
 
4.7%
r 941
 
4.6%
n 923
 
4.5%
o 723
 
3.5%
l 622
 
3.0%
u 466
 
2.3%
Other values (52) 9480
46.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20488
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2897
 
14.1%
e 1389
 
6.8%
a 1107
 
5.4%
t 987
 
4.8%
i 953
 
4.7%
r 941
 
4.6%
n 923
 
4.5%
o 723
 
3.5%
l 622
 
3.0%
u 466
 
2.3%
Other values (52) 9480
46.3%

CITY
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
Boston
541 
Quincy
80 
Cambridge
 
45
Revere
 
42
Chelsea
 
39
Other values (24)
227 

Length

Max length14
Median length6
Mean length6.5954825
Min length4

Characters and Unicode

Total characters6424
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.8%

Sample

1st rowQuincy
2nd rowBoston
3rd rowQuincy
4th rowBoston
5th rowBoston

Common Values

ValueCountFrequency (%)
Boston 541
55.5%
Quincy 80
 
8.2%
Cambridge 45
 
4.6%
Revere 42
 
4.3%
Chelsea 39
 
4.0%
Weymouth 37
 
3.8%
Somerville 25
 
2.6%
Hingham 22
 
2.3%
Winthrop 22
 
2.3%
Brookline 17
 
1.7%
Other values (19) 104
 
10.7%

Length

2024-08-02T12:57:10.303108image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
boston 541
55.4%
quincy 80
 
8.2%
cambridge 45
 
4.6%
revere 42
 
4.3%
chelsea 39
 
4.0%
weymouth 37
 
3.8%
somerville 25
 
2.6%
hingham 22
 
2.3%
winthrop 22
 
2.3%
brookline 17
 
1.7%
Other values (19) 107
 
11.0%

Most occurring characters

ValueCountFrequency (%)
o 1242
19.3%
n 719
11.2%
t 694
10.8%
s 602
9.4%
B 569
8.9%
e 468
 
7.3%
i 237
 
3.7%
r 207
 
3.2%
a 155
 
2.4%
l 151
 
2.4%
Other values (24) 1380
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1242
19.3%
n 719
11.2%
t 694
10.8%
s 602
9.4%
B 569
8.9%
e 468
 
7.3%
i 237
 
3.7%
r 207
 
3.2%
a 155
 
2.4%
l 151
 
2.4%
Other values (24) 1380
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1242
19.3%
n 719
11.2%
t 694
10.8%
s 602
9.4%
B 569
8.9%
e 468
 
7.3%
i 237
 
3.7%
r 207
 
3.2%
a 155
 
2.4%
l 151
 
2.4%
Other values (24) 1380
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1242
19.3%
n 719
11.2%
t 694
10.8%
s 602
9.4%
B 569
8.9%
e 468
 
7.3%
i 237
 
3.7%
r 207
 
3.2%
a 155
 
2.4%
l 151
 
2.4%
Other values (24) 1380
21.5%

STATE
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
Massachusetts
974 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters12662
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMassachusetts
2nd rowMassachusetts
3rd rowMassachusetts
4th rowMassachusetts
5th rowMassachusetts

Common Values

ValueCountFrequency (%)
Massachusetts 974
100.0%

Length

2024-08-02T12:57:10.619890image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T12:57:11.084226image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
massachusetts 974
100.0%

Most occurring characters

ValueCountFrequency (%)
s 3896
30.8%
a 1948
15.4%
t 1948
15.4%
M 974
 
7.7%
c 974
 
7.7%
h 974
 
7.7%
u 974
 
7.7%
e 974
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12662
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 3896
30.8%
a 1948
15.4%
t 1948
15.4%
M 974
 
7.7%
c 974
 
7.7%
h 974
 
7.7%
u 974
 
7.7%
e 974
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12662
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 3896
30.8%
a 1948
15.4%
t 1948
15.4%
M 974
 
7.7%
c 974
 
7.7%
h 974
 
7.7%
u 974
 
7.7%
e 974
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12662
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 3896
30.8%
a 1948
15.4%
t 1948
15.4%
M 974
 
7.7%
c 974
 
7.7%
h 974
 
7.7%
u 974
 
7.7%
e 974
 
7.7%

COUNTY
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
Suffolk County
644 
Norfolk County
155 
Middlesex County
125 
Plymouth County
 
49
Essex County
 
1

Length

Max length16
Median length14
Mean length14.304928
Min length12

Characters and Unicode

Total characters13933
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowNorfolk County
2nd rowSuffolk County
3rd rowNorfolk County
4th rowSuffolk County
5th rowSuffolk County

Common Values

ValueCountFrequency (%)
Suffolk County 644
66.1%
Norfolk County 155
 
15.9%
Middlesex County 125
 
12.8%
Plymouth County 49
 
5.0%
Essex County 1
 
0.1%

Length

2024-08-02T12:57:11.344330image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-02T12:57:11.642433image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
county 974
50.0%
suffolk 644
33.1%
norfolk 155
 
8.0%
middlesex 125
 
6.4%
plymouth 49
 
2.5%
essex 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 1977
14.2%
u 1667
12.0%
f 1443
10.4%
t 1023
7.3%
y 1023
7.3%
974
7.0%
C 974
7.0%
n 974
7.0%
l 973
7.0%
k 799
 
5.7%
Other values (13) 2106
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13933
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1977
14.2%
u 1667
12.0%
f 1443
10.4%
t 1023
7.3%
y 1023
7.3%
974
7.0%
C 974
7.0%
n 974
7.0%
l 973
7.0%
k 799
 
5.7%
Other values (13) 2106
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13933
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1977
14.2%
u 1667
12.0%
f 1443
10.4%
t 1023
7.3%
y 1023
7.3%
974
7.0%
C 974
7.0%
n 974
7.0%
l 973
7.0%
k 799
 
5.7%
Other values (13) 2106
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13933
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1977
14.2%
u 1667
12.0%
f 1443
10.4%
t 1023
7.3%
y 1023
7.3%
974
7.0%
C 974
7.0%
n 974
7.0%
l 973
7.0%
k 799
 
5.7%
Other values (13) 2106
15.1%

ZIP
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct70
Distinct (%)8.4%
Missing142
Missing (%)14.6%
Infinite0
Infinite (%)0.0%
Mean2152.4219
Minimum1801
Maximum2472
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2024-08-02T12:57:12.023566image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1801
5-th percentile2108
Q12121
median2135
Q32163
95-th percentile2215
Maximum2472
Range671
Interquartile range (IQR)42

Descriptive statistics

Standard deviation75.462146
Coefficient of variation (CV)0.03505918
Kurtosis10.687414
Mean2152.4219
Median Absolute Deviation (MAD)17
Skewness2.657886
Sum1790815
Variance5694.5354
MonotonicityNot monotonic
2024-08-02T12:57:12.428424image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2151 41
 
4.2%
2152 27
 
2.8%
2124 27
 
2.8%
2125 26
 
2.7%
2128 23
 
2.4%
2121 19
 
2.0%
2149 19
 
2.0%
2111 19
 
2.0%
2114 19
 
2.0%
2186 18
 
1.8%
Other values (60) 594
61.0%
(Missing) 142
 
14.6%
ValueCountFrequency (%)
1801 1
 
0.1%
1867 1
 
0.1%
1890 1
 
0.1%
2043 15
1.5%
2045 10
1.0%
2060 3
 
0.3%
2066 5
 
0.5%
2108 15
1.5%
2109 12
1.2%
2110 12
1.2%
ValueCountFrequency (%)
2472 7
 
0.7%
2468 1
 
0.1%
2467 18
1.8%
2466 1
 
0.1%
2460 2
 
0.2%
2459 2
 
0.2%
2453 1
 
0.1%
2446 1
 
0.1%
2445 2
 
0.2%
2215 15
1.5%

LAT
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct974
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.337359
Minimum42.204921
Maximum42.495464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2024-08-02T12:57:12.827703image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum42.204921
5-th percentile42.242428
Q142.313366
median42.343794
Q342.371594
95-th percentile42.399264
Maximum42.495464
Range0.29054326
Interquartile range (IQR)0.058228083

Descriptive statistics

Standard deviation0.047593898
Coefficient of variation (CV)0.0011241584
Kurtosis0.51206075
Mean42.337359
Median Absolute Deviation (MAD)0.029053783
Skewness-0.51212007
Sum41236.587
Variance0.0022651792
MonotonicityNot monotonic
2024-08-02T12:57:13.222582image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.29093738 1
 
0.1%
42.34026897 1
 
0.1%
42.32729951 1
 
0.1%
42.36111382 1
 
0.1%
42.37615968 1
 
0.1%
42.39360566 1
 
0.1%
42.36921386 1
 
0.1%
42.31964105 1
 
0.1%
42.38150394 1
 
0.1%
42.31581282 1
 
0.1%
Other values (964) 964
99.0%
ValueCountFrequency (%)
42.20492119 1
0.1%
42.20830838 1
0.1%
42.20934207 1
0.1%
42.20961107 1
0.1%
42.21071825 1
0.1%
42.21152676 1
0.1%
42.21186802 1
0.1%
42.21254372 1
0.1%
42.21338876 1
0.1%
42.21435846 1
0.1%
ValueCountFrequency (%)
42.49546445 1
0.1%
42.49184849 1
0.1%
42.48727483 1
0.1%
42.47695486 1
0.1%
42.47244163 1
0.1%
42.45625452 1
0.1%
42.45475039 1
0.1%
42.45389859 1
0.1%
42.45177113 1
0.1%
42.446131 1
0.1%

LON
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct974
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-71.027524
Minimum-71.165648
Maximum-70.730824
Zeros0
Zeros (%)0.0%
Negative974
Negative (%)100.0%
Memory size7.7 KiB
2024-08-02T12:57:13.629566image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-71.165648
5-th percentile-71.127169
Q1-71.068002
median-71.038397
Q3-70.999202
95-th percentile-70.897932
Maximum-70.730824
Range0.4348238
Interquartile range (IQR)0.068800045

Descriptive statistics

Standard deviation0.06937506
Coefficient of variation (CV)-0.00097673489
Kurtosis2.6350975
Mean-71.027524
Median Absolute Deviation (MAD)0.03489828
Skewness1.1874088
Sum-69180.808
Variance0.004812899
MonotonicityNot monotonic
2024-08-02T12:57:14.015376image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-70.97550306 1
 
0.1%
-71.03841107 1
 
0.1%
-71.03100301 1
 
0.1%
-71.03137398 1
 
0.1%
-71.05265149 1
 
0.1%
-71.03430917 1
 
0.1%
-71.10422457 1
 
0.1%
-71.02726305 1
 
0.1%
-71.04385758 1
 
0.1%
-70.84815222 1
 
0.1%
Other values (964) 964
99.0%
ValueCountFrequency (%)
-71.16564805 1
0.1%
-71.16512791 1
0.1%
-71.16380455 1
0.1%
-71.16136896 1
0.1%
-71.16058687 1
0.1%
-71.15925936 1
0.1%
-71.15877407 1
0.1%
-71.15717571 1
0.1%
-71.15622361 1
0.1%
-71.15545553 1
0.1%
ValueCountFrequency (%)
-70.73082425 1
0.1%
-70.73471526 1
0.1%
-70.74154837 1
0.1%
-70.74164746 1
0.1%
-70.74205298 1
0.1%
-70.74838736 1
0.1%
-70.75239647 1
0.1%
-70.76346235 1
0.1%
-70.77260577 1
0.1%
-70.77347668 1
0.1%

Interactions

2024-08-02T12:56:52.886423image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-02T12:56:50.935992image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-02T12:56:51.782481image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-02T12:56:53.155735image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-02T12:56:51.230672image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-02T12:56:52.072302image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-02T12:56:53.435408image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-02T12:56:51.531745image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-02T12:56:52.361387image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2024-08-02T12:57:14.266711image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
CITYCOUNTYETHNICITYGENDERLATLONMARITALPREFIXRACESUFFIXZIP
CITY1.0000.9880.2030.0680.5190.5300.0000.0260.0800.0000.812
COUNTY0.9881.0000.2380.0650.4760.4590.0000.0430.1030.0990.694
ETHNICITY0.2030.2381.0000.0310.0560.0930.0540.0810.1020.0000.154
GENDER0.0680.0650.0311.0000.1330.0410.0000.9990.0000.0000.022
LAT0.5190.4760.0560.1331.000-0.2600.0000.0700.0560.000-0.023
LON0.5300.4590.0930.041-0.2601.0000.0660.0870.0330.0000.027
MARITAL0.0000.0000.0540.0000.0000.0661.0000.7030.0320.0000.000
PREFIX0.0260.0430.0810.9990.0700.0870.7031.0000.0000.0000.000
RACE0.0800.1030.1020.0000.0560.0330.0320.0001.0000.0000.066
SUFFIX0.0000.0990.0000.0000.0000.0000.0000.0000.0001.0000.000
ZIP0.8120.6940.1540.022-0.0230.0270.0000.0000.0660.0001.000

Missing values

2024-08-02T12:56:53.853562image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-02T12:56:54.617536image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-02T12:56:55.067112image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IdBIRTHDATEDEATHDATEPREFIXFIRSTLASTSUFFIXMAIDENMARITALRACEETHNICITYGENDERBIRTHPLACEADDRESSCITYSTATECOUNTYZIPLATLON
05605b66b-e92d-c16c-1b83-b8bf7040d51f1977-03-19NaNMrs.Nikita578Erdman779NaNLeannon79MwhitenonhispanicFWakefield Massachusetts US510 Little Station Unit 69QuincyMassachusettsNorfolk County2186.042.290937-70.975503
16e5ae27c-8038-7988-e2c0-25a103f01bfa1940-02-19NaNMr.Zane918Hodkiewicz467NaNNaNMwhitenonhispanicMBrookline Massachusetts US747 Conn ThroughwayBostonMassachusettsSuffolk County2135.042.308831-71.063162
28123d076-0886-9007-e956-d5864aa121a71958-06-04NaNMr.Quinn173Marquardt819NaNNaNMwhitenonhispanicMGardner Massachusetts US816 Okuneva Extension Apt 91QuincyMassachusettsNorfolk County2170.042.265177-70.967085
3770518e4-6133-648e-60c9-071eb2f0e2ce1928-12-252017-09-29Mr.Abel832Smitham825NaNNaNMwhitehispanicMRandolph Massachusetts US127 Cole Way Unit 95BostonMassachusettsSuffolk County2118.042.334304-71.066801
4f96addf5-81b9-0aab-7855-d208d3d352c51928-12-252014-02-23Mr.Edwin773Labadie908NaNNaNMwhitehispanicMStow Massachusetts US976 Ziemann GatewayBostonMassachusettsSuffolk County2125.042.346771-71.058813
58e9650d1-788a-78f9-4a28-d08f7f95354a1928-12-25NaNMr.Frankie174Oberbrunner298NaNNaNMwhitehispanicMBoston Massachusetts US303 Bechtelar Bypass Suite 26BostonMassachusettsSuffolk County2467.042.371026-71.118107
6183df435-4190-060e-8f8e-bf63c572b2661957-11-08NaNMrs.Eilene124Walsh511NaNWiegand701MasiannonhispanicFBeijing Beijing Municipality CN235 Lang ParadeCambridgeMassachusettsMiddlesex County2142.042.358928-71.156224
7720560d4-51da-c38c-ee90-c15935278df11972-06-27NaNMr.Lowell343Price929NaNNaNMwhitenonhispanicMLowell Massachusetts US694 Kuhlman Corner Apt 74QuincyMassachusettsNorfolk County2170.042.297904-71.015983
8217851b0-5f47-d376-18b9-0fe4ba77207e1954-03-06NaNMr.Adrian111Gleason633NaNNaNSblackhispanicMBoston Massachusetts US808 Gottlieb WallBostonMassachusettsSuffolk County2126.042.384084-71.100689
9ff331e5c-ab16-e218-f39a-63e11de1ed751927-07-10NaNMr.Eugene421Abernathy524NaNNaNMnativehispanicMPembroke Massachusetts US706 Connelly Track Unit 1BostonMassachusettsSuffolk County2111.042.358519-71.078598
IdBIRTHDATEDEATHDATEPREFIXFIRSTLASTSUFFIXMAIDENMARITALRACEETHNICITYGENDERBIRTHPLACEADDRESSCITYSTATECOUNTYZIPLATLON
964c06513da-7f35-b4eb-5bab-28c87ff97a101959-03-042014-11-16Mr.Tomas436Hermann103NaNNaNMasiannonhispanicMWorcester Massachusetts US754 Pfannerstill ParkMedfordMassachusettsMiddlesex County2155.042.416156-71.125391
9656b6e8cda-703a-6a17-4943-b3bd1d7090841944-03-08NaNMr.Nigel915Carroll471NaNNaNSwhitenonhispanicMMilford Massachusetts US380 Wintheiser Run Apt 92BostonMassachusettsSuffolk County2110.042.391092-71.042205
966a2b011b1-ca0a-69fa-6906-6c06ad18376a1944-06-22NaNMr.Gerald181Murray856NaNNaNMwhitenonhispanicMChelmsford Massachusetts US952 Wyman Frontage road Suite 59BostonMassachusettsSuffolk County2136.042.336430-71.056928
9677506d350-0f35-7c82-3b8a-d7aa801143521942-05-182018-03-29Mrs.Maren639Breitenberg711NaNRitchie586MwhitenonhispanicFRehoboth Massachusetts US460 Padberg Dale Apt 89BostonMassachusettsSuffolk County2131.042.325310-71.091714
9685936f828-81d9-1a90-03b1-cfe49183dba81942-05-18NaNMrs.Sunni15Olson653NaNNitzsche158MwhitenonhispanicFBoston Massachusetts US797 Shanahan CenterBostonMassachusettsSuffolk County2136.042.318959-71.051754
969d684571e-a784-ef61-429e-06fa0d2b16371924-03-15NaNMr.Chris95Fisher429NaNNaNSwhitenonhispanicMFranklin Massachusetts US810 Yundt Forge Suite 2MedfordMassachusettsMiddlesex County2145.042.357626-71.040837
97013c6f26e-17b7-f534-04db-78a26b26018d1940-10-31NaNMrs.Berneice173Heaney114NaNHermiston71MwhitenonhispanicFTempleton Massachusetts US617 MacGyver PathwayBostonMassachusettsSuffolk County2152.042.331490-71.039520
971521e998b-ff0e-767f-b0ee-2bdf1168d66c1943-04-18NaNMr.Jamal145VonRueden376NaNNaNMwhitenonhispanicMChelmsford Massachusetts US505 Mertz Path Apt 40BostonMassachusettsSuffolk County2134.042.341971-71.040624
972b57e24a2-2e48-12f9-3293-c88745cfdc3f1941-04-28NaNMrs.Chrissy459Rempel203NaNBeer512MasiannonhispanicFNeedham Massachusetts US366 Beer CrossroadCambridgeMassachusettsMiddlesex CountyNaN42.337040-71.094676
973204f8028-72f8-d6f8-761f-79ebf9f023111923-02-14NaNMrs.Melaine933Hintz995NaNBaumbach677MwhitenonhispanicFSouthwick Massachusetts US382 Mosciski RoadBostonMassachusettsSuffolk County2128.042.357333-71.057372